Artificial Intelligence Engineer
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Key skills for this role
About the Role
Xebia seeks a Senior AI Engineer to design and develop enterprise-grade AI solutions, focusing on scalable platforms for conversational and transactional AI. The role requires deep AI implementation experience, technical leadership, and collaboration with cross-functional teams.
Key Skills for This Role
Responsibilities
- Design, develop, and maintain scalable AI solutions supporting conversational and transactional business functions
- Contribute to architecture of AI platforms that are modular and extensible for adoption across multiple business units
- Evaluate and recommend appropriate AI tools, models, frameworks, and platforms based on use case requirements
- Implement orchestration patterns that coordinate AI agents, models, and workflows
- Build and maintain integrations between AI components and enterprise systems (CRM, ERP, document management, HR platforms, communication tools)
- Design and implement APIs and integration patterns for seamless connectivity between AI capabilities and consuming applications
- Implement RAG pipelines, agentic workflows, and multi model coordination patterns
- Ensure AI solutions are engineered for performance, reliability, and maintainability in production
- Develop conversational AI capabilities including intelligent chatbots, virtual assistants, and LLM powered dialogue systems
- Build transactional AI functions for document processing, data retrieval, approvals, and workflow automation
- Apply responsible AI principles throughout development lifecycle
- Adhere to AI governance practices covering model versioning, monitoring, and auditability
Requirements
- Bachelor's or Master's degree in Computer Science, AI, Data Science, Software Engineering, or related field
- Minimum 6 8 years of overall experience in software or AI engineering
- Minimum 3 4 years of hands on experience designing and implementing production AI solutions in an enterprise environment
- Proven experience working on AI platforms or solutions that span conversational and process automation use cases
- Experience contributing to or leading technical delivery of AI integration projects involving multiple enterprise systems
- Expertise in AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, LlamaIndex)
- Expertise in large language models (OpenAI GPT, Azure OpenAI, Anthropic Claude, Gemini, or open source LLMs)
- Expertise in vector databases (Pinecone, Weaviate, Azure AI Search, pgvector)
- Expertise in Azure cloud services (Azure OpenAI Service, Azure AI Studio, Azure Bot Services, Azure API Management, Azure Functions, Azure Logic Apps)
- Expertise in Azure infrastructure (AKS, Azure Container Apps, Azure Service Bus, Azure Key Vault, Azure Monitor)
- Proficiency in Python (primary) and at least one of JavaScript, C#, or Java
- Experience with REST API design, event driven architecture, webhook patterns, API gateway management
Full Job Posting
About the Role
- Seeking a highly skilled and experienced Senior AI Engineer / Technical Lead to join the AI & Digital Innovation team.
- The role focuses on designing and developing enterprise grade AI solutions with scalable, extensible platforms for conversational and transactional AI.
- This is a hands on technical role combining deep AI implementation experience with technical leadership and cross functional collaboration.
Key Responsibilities
- Design, develop, and maintain scalable AI solutions for conversational and transactional business functions.
- Contribute to architecture of modular and extensible AI platforms for adoption across business units.
- Evaluate and recommend AI tools, models, frameworks, and platforms based on use case requirements.
- Implement orchestration patterns coordinating AI agents, models, and workflows.
- Build and maintain integrations between AI components and enterprise systems (CRM, ERP, document management, HR, communication tools).
- Design and implement APIs and integration patterns for seamless connectivity.
- Implement RAG pipelines, agentic workflows, and multi model coordination patterns.
- Ensure AI solutions are engineered for performance, reliability, and maintainability in production.
- Develop conversational AI capabilities including chatbots, virtual assistants, and LLM powered dialogue systems.
- Build transactional AI functions for document processing, data retrieval, approvals, and workflow automation.
- Support design of solutions handling both real time and batch AI processing.
- Apply responsible AI principles: fairness, transparency, explainability, data privacy.
Required Qualifications & Experience
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related technical discipline.
- Minimum 6–8 years of overall experience in software or AI engineering.
- Minimum 3–4 years of hands on experience designing and implementing production AI solutions in an enterprise environment.
- Proven experience working on AI platforms or solutions spanning conversational and process automation use cases.
- Experience contributing to or leading technical delivery of AI integration projects involving multiple enterprise systems.
- Expertise in AI orchestration frameworks: LangChain, Semantic Kernel, AutoGen, LlamaIndex, or equivalent.
- Expertise in large language models: OpenAI GPT, Azure OpenAI, Anthropic Claude, Gemini, or open source LLMs (LLaMA, Mistral).
- Expertise in vector databases: Pinecone, Weaviate, Azure AI Search, pgvector, or equivalent.
- Cloud expertise in Azure: Azure OpenAI Service, Azure AI Studio, Azure Bot Services, Azure API Management, Azure Functions, Azure Logic Apps.
- Azure infrastructure: Azure Kubernetes Service (AKS), Azure Container Apps, Azure Service Bus, Azure Key Vault, Azure Monitor.
- Programming languages: Python (primary), with working proficiency in at least one of JavaScript, C#, or Java.
- API & Integration: REST API design, event driven architecture, webhook patterns, API gateway management.
Preferred Qualifications
- Hands on experience with Microsoft Azure AI and integration stack: Azure AI Studio, Azure Integration Services, Azure API Management.
- Familiarity with agentic AI design patterns and multi agent coordination frameworks.
- Experience working in regulated industries such as aviation, finance, healthcare, or government.
- Exposure to enterprise integration platforms such as MuleSoft, Azure Integration Services, or equivalent middleware.
- Microsoft Azure certifications: Azure AI Engineer Associate (AI 102) or Azure Developer Associate (AZ 204).
- Familiarity with Power Platform (Power Automate, Power Apps) in context of AI assisted workflows.
Key Competencies
- Technical Depth: Strong hands on engineering capability from concept to working solution.
- Problem Solving: Structured and pragmatic approach to complex and ambiguous technical challenges.
- Communication: Able to explain technical concepts clearly to technical and non technical stakeholders.
- Collaboration: Works well within cross functional teams and across business units.
- Innovation Mindset: Actively follows AI developments and brings relevant ideas to the team.
- Ownership: Takes responsibility for quality and reliability of solutions developed.
- Mentorship: Committed to uplifting team capability through knowledge sharing and hands on guidance.
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